IVIFCM-TOPSIS for bank credit risk assessment
Result description
Bank credit risk assessment is performed by credit rating agencies in order to reduce information asymmetry in financial markets. This costly process has been automated in earlier studies by using systems based on machine learning methods. However, such systems suffer from interpretability issues and do not utilize expert knowledge effectively. To overcome those problems, multi-criteria group decision-making (MCGDM) methods have recently been used to simulate the assessment process performed by the committee of multiple credit risk experts. However, standard MCGDM methods fail to consider high uncertainty inherently associated with the assessment and do not work effectively when the assessed credit risk criteria interact with each other. To address these issues, we propose MCGDM model for bank credit risk assessment that has two advantages: (1) The imprecise assessment criteria are represented by interval-valued intuitionistic fuzzy sets, and (2) the interactions among the criteria are modeled using fuzzy cognitive maps. When combined with traditional TOPSIS approach to ranking alternatives, we show that the proposed model can be effectively applied to assess bank credit risk.
Keywords
TOPSISInterval-valued intuitionistic fuzzy setsFuzzy cognitive mapsDecision support systemassessmentBank credit risk
The result's identifiers
Result code in IS VaVaI
Result on the web
https://link.springer.com/chapter/10.1007/978-981-13-8311-3_9
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
IVIFCM-TOPSIS for bank credit risk assessment
Original language description
Bank credit risk assessment is performed by credit rating agencies in order to reduce information asymmetry in financial markets. This costly process has been automated in earlier studies by using systems based on machine learning methods. However, such systems suffer from interpretability issues and do not utilize expert knowledge effectively. To overcome those problems, multi-criteria group decision-making (MCGDM) methods have recently been used to simulate the assessment process performed by the committee of multiple credit risk experts. However, standard MCGDM methods fail to consider high uncertainty inherently associated with the assessment and do not work effectively when the assessed credit risk criteria interact with each other. To address these issues, we propose MCGDM model for bank credit risk assessment that has two advantages: (1) The imprecise assessment criteria are represented by interval-valued intuitionistic fuzzy sets, and (2) the interactions among the criteria are modeled using fuzzy cognitive maps. When combined with traditional TOPSIS approach to ranking alternatives, we show that the proposed model can be effectively applied to assess bank credit risk.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Intelligent Decision Technologies 2019 : Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Vol. 1
ISBN
978-981-13-8310-6
ISSN
2190-3018
e-ISSN
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Number of pages
10
Pages from-to
99-108
Publisher name
Springer Nature
Place of publication
Heidelberg
Event location
St. Julians
Event date
Jun 17, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—
Result type
D - Article in proceedings
OECD FORD
Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Year of implementation
2019